Dynamically estimating a propagation time between a first node and a second node of a wireless network

ABSTRACT

Apparatuses, methods, and systems for dynamically estimating a propagation time between a first node and a second node of a wireless network are disclosed. One method includes receiving, by the second node, from the first node a packet containing a first timestamp representing the transmit time of the packet, receiving, by the second node, from a local time source, a second timestamp corresponding with a time of reception of the first timestamp received from the first node, calculating a time difference between the first timestamp and the second timestamp, storing the time difference between the first timestamp and the second timestamp, calculating a predictive model for predicting the propagation time based the time difference between the first timestamp and the second timestamp, and estimating the propagation time between the first node and the second node at a time by querying the predictive model with the time.

RELATED PATENT APPLICATIONS

This patent application claims priority to Provisional PatentApplication Ser. No. 62/890,553 filed Aug. 22, 2019, and to ProvisionalPatent Application Ser. No. 62/914,438 filed Oct. 12, 2019, which areherein incorporated by reference.

FIELD OF THE DESCRIBED EMBODIMENTS

The described embodiments relate generally to wireless communications.More particularly, the described embodiments relate to systems, methodsand apparatuses for dynamically estimating a propagation time between afirst node and a second node of a wireless network.

BACKGROUND

Current data networks are designed primarily for human users and thenetwork and traffic characteristics that human users generate. Thegrowth and proliferation of low-cost embedded wireless sensors anddevices pose a new challenge of high volumes of low bandwidth devicesvying for access to limited network resources. One of the primarychallenges with these new traffic characteristics is the efficiency atwhich the shared network resources can be used. For common low bandwidthapplications such a GPS tracking, the efficiency (useful/useless dataratio) can often be below 10%. This inefficiency is the result of largevolumes of devices communicating in an uncoordinated environment.Addressing this problem is fundamental to the future commercialviability of large-scale sensor network deployments.

It is desirable to have methods, apparatuses, and systems fordynamically estimating a propagation time between a first node and asecond node of a wireless network.

SUMMARY

An embodiment includes a method of dynamically estimating a propagationtime between a first node and a second node of a wireless network. Themethod including receiving, by the second node, from the first node apacket containing a first timestamp representing the transmit time ofthe packet, receiving, by the second node, from a local time source, asecond timestamp corresponding with a time of reception of the firsttimestamp received from the first node, calculating a time differencebetween the first timestamp and the second timestamp, storing the timedifference between the first timestamp and the second timestamp,calculating, by the second node, a predictive model for predicting thepropagation time based the time difference between the first timestampand the second timestamp, and estimating, by the second node, thepropagation time between the first node and the second node at a time,comprising querying the predictive model with the time.

Another embodiment includes a node. The node is operative to receivefrom a first node a packet containing a first timestamp representing thetransmit time of the packet, receive from a local time source, a secondtimestamp corresponding with a time of reception of the first timestampreceived from the first node, calculate a time difference between thefirst timestamp and the second timestamp, store the time differencebetween the first timestamp and the second timestamp, calculate apredictive model for predicting the propagation time based the timedifference between the first timestamp and the second timestamp, andestimate the propagation time between the first node and the second nodeat a time, comprising querying the predictive model with the time.

Other aspects and advantages of the described embodiments will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the described embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a plurality of hubs that communicate data of data sourcesthrough a satellite link to a base station, according to an embodiment.

FIG. 2 shows a sequence of transmission of a packet between a basestation and a hub, according to an embodiment.

FIG. 3 shows examples of characteristics of the propagation delay,according to an embodiment.

FIG. 4 shows a predictive model for estimating the propagation delay,according to an embodiment.

FIG. 5 shows a wireless system that includes external sensorobservations including one or more of location, orientation,acceleration, and other spatial/momentum sensors that aid in estimatingthe propagation delay, according to an embodiment.

FIG. 6 is a flow chart that includes steps of a method of estimating apropagation time between a first node and a second node of a wirelessnetwork, according to an embodiment.

FIG. 7 shows a time-line of interactions between a data source, a hub, abase station and a network server, according to an embodiment.

FIG. 8 is a flow chart that includes steps of a method of determiningwhether to allocate virtual preambles for data reporting, according toan embodiment.

FIG. 9 shows a time-line of interactions between a hub that includes adata source, a base station and a network server, according to anembodiment.

FIG. 10 shows flow charts that includes processes in which a hubconnects to a satellite, according to an embodiment.

FIG. 11 is a flow chart that includes steps of a method of datareporting, according to an embodiment.

FIG. 12 shows data profiles, according to an embodiment.

FIG. 13 shows a plurality of hubs that communicate data of data sourcesthrough a shared resource to a base station, according to an embodiment.

DETAILED DESCRIPTION

The embodiments described include methods, apparatuses, and systems fordynamically estimating a propagation time between a first node and asecond node of a wireless network. At least some embodiments includecalculating a predictive model for predicting the propagation time. Fora least some embodiments, the predictive model is calculated based onone or more of time differences between a first timestamp of a transmittime of a packet, and a second timestamp of the receive time of thepacket. For an embodiment, the propagation time between the first nodeand the second node at a time is estimated by querying the predictivemodel with the time. Various embodiments additionally utilize otherparameters in calculating the predictive model. For at least someembodiments, the predictive model adaptively changes over time.

FIG. 1 shows a plurality of hubs 110 190 that communicate data of datasources through a satellite link (through satellite 191) to a basestation 140, according to an embodiment. For an embodiment, a networkprovider server 170 operates to generate scheduling of the wirelesscommunication between the base station 140 and the plurality of hubs110, 190 through wireless links 115, 116. For an embodiment, the networkprovider server 170 may access a database 160 of, for example, a networkmanagement element 150, aid in generating the schedule communication,and provide the scheduled communication to the base station 140. For anembodiment, the scheduled communication includes allocating frequencyand time slots for both uplink and downlink wireless communication. Foran embodiment, the base station 140 includes a modem 145 and the hubs110, 190 include modems 130, 132, for enabling the wirelesscommunication between the base station 140 and the hubs 110, 190.

For satellite systems, the wireless links 115, 116 can be very long.Accordingly, the propagation delays of wireless communication betweenthe hubs 110, 190 and the base station 140 can be very long. Further,the propagation delay can vary with time.

Satellite motion and a large cell size of satellite beams createssituations in which there are both a large mean RTT (round trip time)(˜512 ms) and also a large variation of the RTT (˜+−4 ms over 24 hrs).The long-term motion of a satellite is not indefinitely predictable andmay even incur sudden changes in position from station keepingactivities. For example, a satellite may maintain its orbital positionby using onboard thrusters to keep its current stationed position. Dueto these reasons, a UE (user equipment) or hub requires a live anddynamic means to independently determine the one-way delay (propagationdelay) between itself and the base station. For an embodiment, RTT iscomputed as twice of estimated one-way (propagation) delay.

It is to be understood that synchronization between communicatingdevices is critical to maintaining wireless communication betweendevices, such as, a first node and a second node, or a base station anda hub. The large variable propagation delays between the devices mayinhibit the ability to maintain synchronization between the devices. Forat least some embodiments, having well characterized (well estimated)propagation time allows the latency of the wireless communication system(including first node and the second node) to be reduced.

Many wireless systems include a timing “slush fund” to match the timinguncertainty of the wireless system. The described embodiments forestimating the propagation delay can be used to reduce the timinguncertainty, and therefore, maintain the timing of wirelesscommunication within the slush fund. Other related systems, such ashigh-frequency trading can also benefit from controlling system latency,and to do that these systems need to maximize propagation timecharacterizations. For at least some embodiments timing slush fundsconsume network resources which could have been used for actualapplication data, which can reduce network throughput. As will bedescribed, the estimations of the propagation delay can allow theutilization of virtual preambles, as which will be described, reducesthe demands on the resources (frequency spectrum) needed by the wirelesssystem.

FIG. 2 shows a sequence of transmission of a packet between a basestation and a hub, according to an embodiment. The packet sequenceallows for dynamic estimation of the propagation delay 250 between afirst node (base station 140) and a second node (hub 110) of a wirelessnetwork.

As shown, a packet 210 is transmitted from the base station 140 to thehub 110. For an embodiment, a first time stamp is included within thepacket 210 by the base station 140 that provides an indication of thetransmit time of the packet 210 from the base station 140. The hub 110receives the packet 210 and the first timestamp.

For an embodiment, the hub 110 receives a second timestamp from a localsource 220 of the hub 110 that corresponds with a time of wirelessreception of the first timestamp received from the base station 140. Thelocal source 220 of FIG. 2 is shown as being internal to the hub 110,but the local source 220 does not have to be internal to the hub 110.For an embodiment, a controller 268 of the hub 110 operates to calculatea time difference between the first timestamp and the second timestamp.Further, the controller 268 operates to store the time differencebetween the first timestamp and the second timestamp in memory 230. Foran embodiment, the controller 268 additionally stores a time of thecalculating of the time difference.

For an embodiment, the process of receiving first timestamps from thebase station is repeatedly performed over time, providing a large (longtime) or continuous characterization of the propagation delay betweenthe base station 140 and the hub 110. As will be described, for anembodiment, the first timestamp is estimated based upon the firstreceived timestamp from first node and based-upon forward integrating acounting signal received from the first node.

For an embodiment, a predictive model is calculated based on one or moreof time differences between the first timestamp and the secondtimestamp. For an embodiment, the predictive model is additionallycalculated based on the times of calculating one or more timedifferences. For at least some embodiments, calculating the predictivemodel and estimating the propagation time are two asynchronous eventswith respect to each other.

For an embodiment, the predictive model is used for predicting thepropagation time at a present time, and/or at future times. Anembodiment includes estimating the propagation time between the firstnode and the second node at a time (current or future) by querying thepredictive model with the current (or future) time at the hub 110.

FIG. 3 shows examples of characteristics of the propagation delay,according to an embodiment. As shown, a component 310 of the propagationdelay may be well behaved a predictable. For example, the component ofthe propagation delay due to the travel time between the hub and thebase station due to variances in the distance from the satellite willvary continuously. Further, as shown components 320 of the propagationdelay are not continuous and predictable. These delays may be as aresult, for example, of motion of the hub itself. The component 310 addswith the components 320 yielding a composite propagation delay 330 overtime that includes both the predictable and the unpredictablepropagation delays.

For an embodiment, the predictability of propagation delay between thefirst node and the second node is a function of the frequency of newinformation being injected into a prediction model. For example, if thesystem dynamics result in a slowly changing system (that is, slowlychanging propagation delay), the model is accurately predictable forlower frequency injections of new pieces of information. Thevalidity/predictability of the propagation delay prediction model isproportionally related to the new information frequency and the rate ofchange of the system dynamics.

For at least some embodiments, the sampled data injected into theprediction model is 2-dimensional, including the calculated timedifference between the first timestamp and the second time stamp, andthe time of the calculation of the time difference. The purpose of thetwo-dimensionality is to accommodate for variance and uncertainty inperiodicity of information injection into the propagation delayprediction model. For example, the prediction model may receive 5consecutive samples, wherein new information is injected every 10seconds, and for the 6^(th) instance there is a 20 second gap.

The internal (predictive) model could take on a number of differentforms depending upon the system dynamics in which it is describing. Somemodels are better suited than others for different real-world systems.Accordingly, at least some embodiments include adaptively selecting abase model based on characteristics of the first time stamp and thesecond time stamp, and/or other information available related to thepropagation delay between the first and second nodes.

For an embodiment, the predictive model is as simple as a constant modelor passthrough model. For at least some embodiments, queries of thepredictive model give that last received time difference.

Depending upon the time number and how recently the time differencecalculations are available, the order of the model (that is, how manyderivatives or higher power terms) may dynamically vary. In oneinstance, when a model is first initiated and only one data point isavailable, the model may utilize a zeroth order estimation technique,however as additional data points become available 1^(st), 2^(nd), and3^(rd) order terms may be utilized to increase the fidelity of thepredictive model and to increase the time-period of validity of thepredictive model by capturing higher-order system dynamics. For anembodiment, the frequency of data sampling and model updating can alsoallow more of the underlying system dynamics to be captured and modeled.This is very much related to Nyquist frequency.

In practicality it is often not easy to know (by the hub) what networktime (what time the base station think it is). As previously described,wireless communication between the hub and the base station through thewireless link demands synchronization of the hub with the base station.In reality it is not desirable to receive a new timestamp from the basestation every X seconds. An embodiment includes the second node (hub)receiving one or more first time timestamps from the base station once,or very infrequently. For an embodiment, the hub then uses wellcharacterized and non-divergent discrete networking timing increment“ticks” to forward integrate network time. For an embodiment, thediscrete “tick” comes in the form of the current operating frame numberof the system. The challenge is that the frame number can be ambiguousbecause frame numbers are cyclical (that is, 1 2 3 4 5 . . . 1 2 3 4 5).

For an embodiment the discrete network counting ticks include cyclicalframe counters, for this embodiment the first time stamp is estimatingby selecting from a group of possible cycle counts a value whichproduces a propagation time that is within a predefined acceptable valuerange. Given an expectation around propagation time, there exists aunique solution for how many frame number cycles have occurred over alarge, but finite, time period.

FIG. 4 show a predictive model of two different control loops 410, 420for estimating the propagation delay, according to an embodiment.

Predictive Model(s)

Due to the large RTT (propagation delay) drift (up to ˜1.2 μs/s) a newRTT must be calculated and sent to the modem of the second node (hub) ata frequency high enough to allow adjustment for drift of the propagationdelay between the first node (base station) and the second node (hub).This can place a large burden on the requirement and availability of aGNSS (Global Navigation Satellite System) receiver of, for example, thehub. However, estimation of the RTT drift can be simplified due to thewell-behaved and characterizable motion of the satellite within thewireless link between the first node (base station) and the second node(hub).

FIG. 4 shows an embodiment of a nested loop model for RTT calculation(Loop1). For an embodiment, the exterior loop 410 consists of timedifferences (R_(i)) being calculated by taking the difference betweenthe Network Time 442 (at the first node or base station) and Local Time444 (at the second node or hub) during an NB-IoT (Narrow Band Internetof Things) modem sleep cycle. For an embodiment, this time delta R_(i)is sent to a local primitive RTT model 446 (that is, the propagationdelay predictive model). For an embodiment, the RTT model 446 providesan equation for the RTT based upon the current GNSS time (0.5 ppm->1 ppmclock drift poses negligible accuracy concerns as an input to the RTTmodel 446) and a series of the i most recent time deltas. The inner loop420 consists of the RTT model (executed on NB-IoT chipset) pushing a newRTT to the modem every <1 second. A key observation of this method isthat new RTT values can be sent to the modem without the modem goinginto sleep modem. There is still a freshness requirement on the RTTmodel which requires new GNSS readings on a periodic basis, but theinclusion of the model reduces the overall sample frequency requirementof the local GNSS and disconnects taking GNSS readings with updating theRTT.

For an embodiment, the modem of the second node (hub) 448 and the GNSSreceiver of the second node utilize the same antenna and RF chain withinthe second node.

For an embodiment, the UE (user equipment) or hub or second nodeperforms a R_(i) (difference between the first time stamp and the secondtime stamp) measurement using a GNSS timestamp and network timeavailable from SIB16 and frame counter. For an embodiment, the UErequires c-DRX (3GPP Defined sleep modes) and e-DRX sleep mode (toenable cohabitation between a GNSS receiver and a modem using the sameRF chain to support a GNSS measurement. For an embodiment, the frequencyof the R_(i) measurements depends on the sleep cycle. A required sleepduration<10.24 s. (A short sleep cycle is desirable, because sleep cycleduration adds latency to any communications sent across the network.However, the sleep cycle must also be long enough to accurately capturea GNSS reading).

For an embodiment, whenever a TA (timing advance) correction isavailable from the base station, it should be used to correct themeasured delay, in addition it can be used to adjust the frequency ofLoop1 410 or loop 2 420 of FIG. 4.

For an embodiment, the RTT (propagation delay) is calculated using thepredictive model based upon a finite and limited series of previousR_(i) measurements. For an embodiment, the predictive model produces anRTT output given an input of current GNSS time. For an embodiment, thisprocess occurs at a high frequency cycle (1 Hz) and can occur even whenthe modem is not in sleep mode.

FIG. 5 shows a wireless system that includes external sensorobservations including one or more of location, orientation,acceleration, and other spatial/momentum sensors that aid in estimatingthe propagation delay, according to an embodiment. Here, a hub 110includes a controller 568 receiving sensor input, such a, a locationsensor 562, an orientation sensor 564, and/or an acceleration sensor566.

At least some embodiments include additional information as inputs forgenerating the predictive model for predicting the propagation time.That is, in addition to calculating the predictive model based on one ormore of time differences between the first timestamp and the secondtimestamp, other parameters, such as, location or accelerometer data maybe utilized. This is motivated in the satellite context, in that therate of change of the propagation time is the summation of thewell-behaved motion of the satellite and the poorly-behaved motion ofthe hub. The hub is poorly behaved because it introduces human-triggeredsystem dynamics. For an embodiment, the motion of the hub may becaptured by GPS information, and it may be captured by accelerometerdead-reckoning techniques. When this information is provided to thepredictive model, sensor-fusion techniques can produce a more accurateoutput.

FIG. 6 is a flow chart that includes steps of a method of estimating apropagation time between a first node and a second node of a wirelessnetwork, according to an embodiment. A first step (a) 610 includesreceiving, by the second node, from the first node a packet containing afirst timestamp representing the transmit time of the packet. A secondstep (b) 620 includes receiving, by the second node, from a local timesource, a second timestamp corresponding with a time of reception of thefirst timestamp received from the first node. A third step (c) 630includes calculating a time difference between the first timestamp andthe second timestamp. A fourth step (d) 640 includes storing the timedifference between the first timestamp and the second timestamp. A fifthstep (e) 650 includes calculating, by the second node, a predictivemodel for predicting the propagation time based the time differencebetween the first timestamp and the second timestamp. A sixth step (f)660 includes estimating, by the second node, the propagation timebetween the first node and the second node at a time (a current time ora future time), comprising querying the predictive model with the time.

At least some embodiments further include storing a time of thecalculating of the time difference. Further, for at least someembodiments, calculating, by the second node, the predictive model isfurther based on the time of calculating the time difference. For anembodiment, the time difference calculation is performed at the secondnode (hub). However, time difference calculation can be performed at anynode (location) that has access to both the first timestamp and secondtimestamp.

At least some embodiments include performing the performing steps a, b,c, d, N successive times for N packets, and calculating the predictivemodel for predicting the propagation time, based on time differencesbetween the first timestamps and the second timestamps of each of the Npackets. For an embodiment, the N packets are a running number ofpackets of a continuous series (could be infinite) of packets.

At least some embodiments include estimating, by the second node,additional first time stamps of additional packets based on the firsttime stamp, and forward integrating counter information provided by thefirst node.

That is, after receiving an initial first time stamp, additional firsttime stamps may be created at the second node. For an embodiment, thefirst node provides a count indicator that allows the second node tocreate first time stamps. For example, after the first completionreception of the first time stamp additional first time stamps fromfirst node may be replaced by a different packet from first noderepresenting a well-characterized counter, to allow the second node tocalculate the additional first timestamps using forward integrationmethods. As an example, the first node sends a first timestamp to secondnode, but afterwards sends a 1 second tick indicator (current radioframe number). The second node would then calculate an effective firsttimestamp in the future by taking the original first timestamp andadding the sum of the number of 1 second tick indicators to thattimestamp. For an embodiment, specifically counting the passage of radioframe.

As previously described, in order for the nodes (hub and the basestation) to communicate, they must be synchronized. For an embodiment,the first node and the second node are locally phase synchronized to thesame time source. For an embodiment, the first node and the second nodeare synchronized to GPS time.

For at least some embodiments, calculating, by the second node, thepredictive model for predicting the propagation time based the timedifference between the first timestamp and the second timestamp includesselecting a regression model base on a-priori information aboutcharacteristics including a cyclic nature of the propagation delaybetween the first node and the second node, and computing parameters ofthe regression model based on at least the time difference between thefirst timestamp and the second timestamp.

For an embodiment, the regression model is selected based upon a-prioriinformation about the form/characteristics (that is, is the propagationdelay time varying, cyclic in nature, and/or stochastic/deterministic)of the propagation delay between the first and second node. For anembodiment, the regression model is computed based on 1 to N availabletimestamp difference calculation data points. Further, for anembodiment, the regression model is computed based on the time(s) of thetime difference calculation(s).

For an embodiment, the propagation delay predictive model is calculatedbased on, for example, two inputs (X,Y)->(Time of DifferenceCalculation, Difference Calculation Value). An exemplary predictivemodel selection includes first selecting an affine Regression model:Y=AX+B (A-prior knowledge dictated an affine model vs polynomial, etc).Secondly, solving for A and B based upon 1 to N available data tuples.Finally, the selected and calculated predictive model Y is queried byinputting a current or future X.

At least some embodiments further include identifying a time recentthreshold between a time of a last time difference calculation and thetime, wherein the time recent threshold is identified based on anestimated rate of change of system dynamics of the first node and thesecond node. That is, as time goes by, the predictive model may becomestale and no longer provide accurate estimates of the propagation delay.The rate that the predictive model because stale or dated is dependenton the rate of change of the system (the first node and the second nodecommunicating) dynamics.

For an embodiment, determining the rate of change of the system includeslinearly estimating an error growth rate by comparing the model error asgiven by the timing advance correction to the elapsed time since themodel was last updated. This error growth rate (which is the differencebetween the actual and modeled rate of change of the system) is thencompared against the timing accuracy requirements of the system toproduce a time period over which model estimations are valid, outside ofthis the model becomes stale.

For an embodiment, the error growth rate may also be estimated fromhistorical readings/values, and the error growth may also beincrementally estimated based upon readings from the sensors such asaccelerometer/GPS (location). That is, a stationary hub may have anerror growth rate of X, when the hub is moving (which can be determinedfrom sensors) and the error growth rate may be determined as X+Y.

As previously described, for an embodiment, the first node comprises abase station and the second node comprises a hub. Further, for anembodiment, a wireless communication link is formed between the basestation and the hub. Further, for an embodiment, a satellite facilitatesthe wireless communication link between the base station and the hub.

As previously described, at least some embodiments further include thesecond node using the predicted propagation time to facilitate wirelesscommunication between the second node and the first node by enablingsynchronized reception timing of wireless communication from the secondnode to the first node. For an embodiment, synchronized reception timingof transmissions from second node to first node includes aligning radioframes.

For at least some embodiments, how frequently additional firsttimestamps and second time stamps are received by the second node isselected based on a determined error in estimates of the propagationdelay. For at least some embodiments, the error in propagationprediction is calculated is by internal network protocol timing finecorrection schemes. In the case of LTE (long term evolution) this is a“Timing Advance”. For at least embodiments, the predictive model is tosolve for both coarse and fine timing. The network can naturally resolvefine timing if needed but cannot solve for coarse timing. However, it isdesirable to the network to not have to solve for fine timing.

For at least some embodiments, how frequently additional first timestampand second time stamps are received by the second node is selected basedon at least one of user or hub-initiated commands and configurations, oran ephemeris of a satellite within a wireless link between the secondnode and the first node. For at least some embodiments, user or hubinitiated commands and configurations include, for example, a situationin which the hub is first turned on, and it is desirable to generate anaccurate model as quickly as possible to reduce boot time. During thisboot time the hub may instruct the system to increase the frequency ofmodel updating to reduce latency. Once an adequate model is in place thehub may instruct the system to slow the rate of model updating toconserve resources. For at least some embodiments, the hub can alsoinitiate changes based upon the previously described observedpropagation error characteristics. Not all Geo-stationary satellites arestationary. Over the lifespan of a satellite it may drift in orbit orinclination increasing or decreasing its relative motion with first nodeand the second not as a function of time. With little relative motionthe frequency of model updates can decrease and vice versa. For at leastsome embodiments, how frequently additional first timestamp and secondtime stamps are received by the second node is selected based onexternal sensor observations including one or more of location,orientation, acceleration, and other spatial/momentum sensors.

The propagation delay between two nodes in a satellite system changes ata rate which is the summation of the rate of change induced by motion inthe satellite and the rate of change induced by motion of either of thenodes. When the nodes are stationary the system dynamics change moreslowly and model updates can be calculated more slowly, when the nodesare in motion the system dynamics change more quickly and model updatesmust be performed more quickly.

For at least some embodiments, how frequently additional first timestampand second time stamps are received by the second node is selected basedon characteristics of transmitted data. Accurate propagation models areonly required when the second not wants to send a message to first node.For an embodiment, known data transmission characteristics of the secondnode determine when these communications take place.

At least some embodiments further include selecting how frequently toestimate the propagation delay between the first node and the secondnode at additional times, and accordingly querying the predictive modelwith the future times based on a determined error in estimates of thepropagation delay. The error estimates can be determined as previouslydescribed.

At least some embodiments further include selecting how frequently toestimate the propagation delay between the first node and the secondnode at additional times, and accordingly querying the predictive modelwith the future times based on at least one of user or hub-initiatedcommands and configurations, or an ephemeris of a satellite within awireless link between the second node and the first node.

At least some embodiments further include selecting how frequently toestimate the propagation delay between the first node and the secondnode at additional times, and accordingly querying the predictive modelwith the future times based on external sensor observations includingone or more of location, orientation, acceleration, and otherspatial/momentum sensors.

At least some embodiments further include selecting how frequently toestimate the propagation delay between the first node and the secondnode at additional times, and accordingly querying the predictive modelwith the future times based on characteristics of transmitted data.

At least some embodiments further include updating the predictive modelbased on a determined error in estimates of the propagation delay.

At least some embodiments further include updating the predictive modelbased on at least one of user or hub-initiated commands andconfigurations, or an ephemeris of a satellite within a wireless linkbetween the second node and the first node.

At least some embodiments further include updating the predictive modelbased on external sensor observations including one or more of location,orientation, acceleration, and other spatial/momentum sensors.

At least some embodiments further include updating the predictive modelbased on characteristics of transmitted data.

For at least some embodiments include the transmission of preambles fromthe second node to the first node. For at least some embodiments, thepreambles are used to identify a desire to transmit data and to performcontention resolution if multiple nodes desire to transmit data at thesame time. Further, for at least some embodiments, preambles are used toperform fine network timing correction.

As will be described, hub profiles allow for identification of a desireto transmit data to be eliminated and to effectively eliminatecontention between multiple nodes. Further, the described propagationestimation processing can also eliminate the requirement to perform finenetwork timing. When both of these requirements are satisfied, preamblesdo not serve a required purpose and can be replaced with virtualpreambles, resulting in a net increase in radio resources available foractually transmitting useful data.

Coarse vs Fine Timing

For an embodiment, a wireless system including a first node and a secondnode has a propagation delay time X. For an embodiment, the coursetiming is an estimate of X with error bounds+−Delta (a first estimatethreshold). For an embodiment, the fine Timing is an estimate of X witherror bounds+−Beta (a second threshold estimate), where Beta<<Delta. Ifthe network has an estimate of X within +−Delta, it can naturallycorrect to an estimate of X within +−Beta using a timing advancecorrection. If the network does not have an estimate of X within +−Deltathe network will not work and bi-direction communication breaks down.The described propagation delay predictive models provide methods whichat a minimum provides an estimate of X within +−Delta, but also canprovide an estimate of X within +−Beta, eliminating the requirement forthe network to perform self-timing corrections, which is performed viasending preambles and receiving timing advance corrections. For at leastsome embodiments, estimates of the propagation delay within an errorbounds between 0 and Beta allows for successful uplink transmission.Maintaining the estimation of the propagation delay by the predictivemodel within +−Beta (within the second estimate threshold) allows forthe elimination of timing advance corrections. For at least someembodiments, a network server provides communication schedules to thedevices (first and second nodes). If the timing advances correctionshave been eliminated due to accuracy of the predictive model inestimating the propagation delay, preambles may be eliminated, andvirtual preambles (to be described) can be used. The utilization of thevirtual preambles is advantageous due to the reduction of the use ofradio (wireless spectrum) resources.

For at least some embodiments, estimations of the propagation delayshaving an error bounds of between Delta (the first estimate threshold)and Beta (the second estimate threshold) include the use of preamblesfor uplink communication between the second node and the first node andvirtual preamble cannot be used. For at least some embodiments,estimations of the propagation delays having an error bounds of lessthan Beta (the second estimate threshold) include the use of virtualpreambles for uplink communication between the second node and the firstnode. As previously mentioned, utilization of virtual preambles isadvantageous due to the reduction of the use of radio (wirelessspectrum) resources.

The embodiments described include methods, apparatuses, and systems forreporting data of data sources. For at least some embodiments, when anerror bounds of estimations of the propagation delay between a firstnode (base station) and a second node (hub) is less than an estimatethreshold Beta (the second estimate threshold) the base station receivesone or more preambles from a set data sources during a scheduled timeslot and receives one or more virtual preambles from a network serverduring the scheduled time slot, wherein the one or more virtualpreambles are associated with another set data sources. In response toreceiving the preambles and the virtual preambles, the base stationgenerates responses which are transmitted to the data sources, whereinthe responses included scheduled time and frequency allocations foruplink communication from the data sources. Once generated, theresponses are transmitted by the base station to the data sources. Foran embodiment, the base station generates an acknowledgement to avirtual preamble which is sent to the network server.

Referring back to FIG. 1, for an embodiment, the hubs 115, 116 generatea preamble when the hubs 115, 116 have data for uplink transmission tothe base station 140. The preamble is transmitted through the wirelesscommunication links 115, 116 to the base station 140. The transmissionof multiple preambles is according to the schedule that specifies atleast time and/or frequency slots. The base station 140 then generates aresponse that includes frequency and time slot for transmission ofuplink data, and unique preamble Id(s) (identification) for each of thepreambles and each of the virtual preambles. For an embodiment, the basestation 140 generates a response that includes frequency and time slotfor transmission of uplink data, unique preamble Id(s) (identification)for each of the preambles and each of the virtual preambles, andscrambling codes. The hubs 110, 116 then transmit the data to the basestation.

The preambles are used to notify the base station of the need of an edgedevice (data device) to transmit data of the shared wirelesscommunication links 115, 116. For at least some embodiments, thepreambles are temporally coordinated (scheduled) to eliminate collisions(wireless interference) during preamble windows (scheduled time slots).

It is to be understood that an optimal network design may not utilizescheduling of preamble. However, for operation within existingstandards, the preambles and virtual preambles of the describedembodiments are schedule to allow operation within an existing framework. In the existing system, preambles can be transmitted in any of therandom access slots using any of the available preambles. However,through scheduling of the preambles, it is transmitted during specificrandom access slots controlled by the trigger function. In addition tothat the trigger function can also define a preamble group from whichthe preamble can be chosen for transmission.

For at least some embodiments, the preambles include identifyinginformation which correspond to a resource size allocation of thescheduling of transmissions through the shared wireless communicationlinks 115, 116. For at least some of the described embodiments,preambles are communicated to the base station 140 over orthogonalfrequencies over the air, whereas the virtual preambles are communicatedto the base station 140 from the network server through an electronicnetwork. For at least some embodiments, the virtual preambles includethe same information as the over-the-air preambles.

For at least some embodiments, the virtual preamble are provided to thebase station 140 temporally coordinated with the coordinated (scheduled)transmission of the preambles. That is, the virtual preambles arecommunicated to the base station from the network server during the samepreamble window as the preambles are wirelessly communicated from thehubs 110, 190 to the base station. Essentially, the base station is“spoofed” into treating the preambles and the virtual preamble the sameway. The virtual preambles need to be provided to the base station 140using the same timing as the preambles. An external applicationoperating on, for example, the network server 170 operates to ensure thetiming of the virtual preambles is correct. For an embodiment, thenetwork server provides the virtual preambles to the base station priorto the scheduled slot along with the timing information which allows thebase station to interpret the virtual preamble at the time of scheduledslot. That is, the virtual preamble is provided to the base stationbefore the schedule time slot, but the base station is provided withadditional timing information that allows the base station to interpretthe virtual preamble at the time of scheduled slot.

It is to be understood that the preambles are transmitted “over the air”and accordingly, occupy valuable available frequency spectrum.Accordingly, the number of preambles that can be allocated may belimited to the number of subcarrier frequency resources available.However, virtual preamble are communicated to the base stationelectrically and do not use any frequency spectrum because they are nottransmitted “over the air”.

For at least some embodiments, other than reception of a virtualpreamble rather than a preamble, the interactions between the basestation and the hub are the same for both preamble and virtualpreambles. For at least some embodiments, the base station responds backto the network server upon the reception of a virtual preamble—inacknowledgement of reception of the virtual preamble.

For at least some embodiments, the implementation and use of the virtualpreamble reduces the data traffic through the shared wirelesscommunication links 115, 116 because the virtual preambles are notcommunicated to the base station 140 through the shared wirelesscommunication links 115, 116. This is particularly beneficial when alarge number of data devices are reporting data through the sharedwireless communication links 115, 116 to the base station 140. Theutilization of virtual preamble reduces the number of preamblestransmitted, and accordingly, reduces network overhead. The use ofvirtual preamble reduces the number of preambles transmitted, andaccordingly, allows more frequency spectrum to be utilized communicatingdata.

For an embodiment, the network server 170 generates a data profile (orhub profile) (121, 122, 123, 124, 125) for each of the hubs 110, 190.For example, the server 170 generates the data profile that the basestation 140 provides to the hub 190. For an embodiment, the data profile(or hub profile) (121, 122, 123, 124, 125) for each of the hubs 110, 190correspond with devices 111, 112, 113, 114, 115 which are connected tothe hubs 110, 190.

For an embodiment, the data profile includes a periodicity, an offset(timing offset), and a carrier frequency based on the scheduledcommunication. For an embodiment, the hub utilizes the periodicity, theoffset, and the carrier frequency of its data profile for determiningwhen and at what carrier frequency to transmit uplink wirelesscommunication to the base station 140. For at least some embodiments,the data profile includes virtual preamble ID. For at least someembodiments, the data profile includes preamble IDs. For at least someembodiments, the preamble IDs includes preamble ID groups, wherein an IDgroup includes multiple usable IDs for the hub.

For example, the data profile may specify a periodic data transmissiononce every 5 minutes, with an offset, which may be represented in thedata profile as: 5.05/5.03. Alternatively, periodicity can be defined interms of a prach (physical random access channel) window. For anembodiment, a prach window is a time slot reserved for either a preambleor a virtual preamble. For an embodiment, a number of prach windows maybe timing between two consecutive triggers. The offset can be defined asfirst prach index in a NBIOT (narrow band, internet of thing) hyperframecycle.

For an embodiment, the base station 140 then receives uplink wirelesscommunication from each of the plurality of hubs 110, 190 according tothe data profile of each of the hubs 110, 190 and according to thescheduled communication. For an embodiment, the hubs 110, 190 use thedata profiles 122, 124 for determining when to transmit, and the basestation 140 uses the scheduled communication to determine when toreceive the uplink wireless communication.

As shown, for an embodiment, the uplink wireless communication istransmitted by plurality of hubs and received by the base stationthrough a satellite wireless link via a satellite 191. As described, forat least some embodiments, the hub includes the data source.

FIG. 7 shows a time-line of interactions between a data source, a hub, abase station and a network server, according to an embodiment. As shown,a data source 710 reports data 750 to a hub 720. An embodiment includeshubs transmitting coordinated preambles to the base station. However, atleast some embodiments further include a virtual preamble 755 beingcommunicated by a network server 740 to the base station 730. As shown,when the virtual preamble 755 is communicated to the base station 730,an over-the-air preamble is not wirelessly transmitted (as depicted by753) through a wireless link to the base station 730.

Once the base station 730 has received an over-the-air wirelesslytransmitted preamble or has received a virtual preamble 755 from thenetwork server 740, the base station transmits a response 760 to thevirtual preamble, and to any wirelessly received preambles. Theresponse(s) include frequency and time slot for transmission of uplinkdata, and unique preamble Id(s) (identification) for each of thepreambles and each of the virtual preambles. For an embodiment, theresponses include frequency and time slot for transmission of uplinkdata, unique preamble Id(s) (identification) for each of the preamblesand each of the virtual preambles, and scrambling codes.

The response 760 is then communicated 770 to the data source. The datasource provides 780 data to the hub 720, which then wirelessly transmitsthe data through uplink communication 790 per the response 760 receivedfrom the base station 730.

FIG. 8 is a flow chart that includes steps of a method of determiningwhether to allocate virtual preambles for data reporting, according toan embodiment. An important element of communication 755 of the virtualpreambles from the network server 740 to the base station 730 is thatthe timing of the communication of the virtual preambles needs to beaccording to the scheduled communication of the preambles—so that thebase station can operate to respond the same to both the preambles andthe virtual preambles.

For at least some embodiments, the virtual preambles are used if atiming advance measurement is not required from the base station. For anembodiment, this is possible only when hub knows the propagation delaybetween hub and base station within a required accuracy.

For at least some embodiments, the data reporting is functionallycontrolled. Exemplary function reporting of the data of the data sourcesinclude periodic reporting, scheduled reporting, a trigger function(that includes, for example a Boolean function), and/or state changetriggering. The first two (periodic reporting, scheduled reporting) arepredictable and coordinated, whereas the second two (trigger function,state change triggering) are less predictable and not coordinated. Thatis, the first two can be temporally predicted, whereas the second twoare condition driven and are not as temporally predicable.

As previously stated, the virtual preambles need to be provided to thebase station within a coordinated time slot. Accordingly, an embodimentincludes generating virtual preambles for data reporting by the datasources that are predictable and coordinated. As shown in FIG. 8, for anembodiment, a first step 810 includes the network (for example, thenetwork server) defining trigger (for reporting of data) functions forthe data sources. Each data source can include multiple triggerfunctions. For each data source, a second step 820 includes determiningwhether the trigger function is coordinated (for example, periodicreporting, scheduled reporting). If yes, then a third step 830 includesdetermining if the timing between the hub and the base station, and thetiming between the network server and the base station are accurate. Ifyes, then a fourth step 840 includes allocating a virtual preamble forthe data reporting per the coordinated trigger function. That is, thereporting is temporally predicable, and generation and communicating ofthe virtual preamble from the network server to the base station can beperformed within an allocated time slot. For this embodiment, thevirtual preamble can arrive at the same time as preamble would at thebase station. The network server should coordinate with the base stationto inject the virtual preamble to the correct slot.

For an embodiment, the third step 830 that includes determining if thetiming between the hub and the base station, and the timing between thenetwork server and the base station are accurate, include characterizingtiming synchronization performance. For an embodiment, the timingsynchronization performance includes determining a wireless uplinktiming synchronization accuracy between the hub and the base station. Iftiming synchronization performance is better than a threshold thenvirtual preamble can be allocated, and if it is worse than thethreshold, than preambles are allocated. In order to support virtualpreambles, uplink timing synchronization should be such that a “timingadvance command” message is not required from the base station. For anembodiment, the base station estimates an uplink timing error and sendsit to hub as a timing advance. The hub uses timing advance to correctits uplink transmission timing for further uplink transmissions. In caseof virtual preambles, since there is no over the air virtual preamble,the timing error is not estimated by base station. Thus, in order tosupport virtual preambles, uplink timing synchronization accuracy shouldbe better than the defined threshold. The timing accuracy performancedepends upon the capability of the hub to track satellite positionand/or measure accurate round trip time of wireless communicationbetween hub and base station through the satellite. That is, for anembodiment, the timing synchronization performance can be determined bymeasuring the round trip time (or propagation delay) between the hub andthe base station. As previously described, for at least someembodiments, estimations of the propagation delays having an errorbounds of between Delta (the first estimate threshold) and Beta (thesecond estimate threshold) include the use of preambles for uplinkcommunication between the second node and the first node and virtualpreamble cannot be used. For at least some embodiments, estimations ofthe propagation delays having an error bounds of less than Beta (thesecond estimate threshold) include the use of virtual preambles foruplink communication between the second node and the first node.

If the trigger function of the data to be reported is not coordinated, afifth step 850 includes allocating a standard non-virtual unscheduledpreamble for reporting of the data. That is, the non-coordinated datareporting is not predictable, and therefore, must be reported by the hubto the base station utilizing a standard non-virtual unscheduledpreamble.

If the trigger function is coordinated, by the time is not accurate, asixth step 860 includes allocating a standard non-virtual scheduledpreamble for the reporting.

FIG. 9 shows a time-line of interactions between a hub that includes adata source, a base station and a network server, according to anembodiment. This time-line accounts for an embodiment in which the hub920 includes the data source that reports has data to report 950.Accordingly, the hub 920 can transmit preambles (not shown) or thenetwork server 940 can communicate virtual preamble 955 to the basestation 930 and the preamble 953 is not sent by the hub 920. Either way,the base station transmits a response 960 to the hub upon receiving apreamble or a virtual preamble. The hub 920 then transmits uplinkcommunication 990 to the base station 930 according to the informationwithin the response 960.

FIG. 10 includes flow charts that includes steps of processes in which ahub connects to a satellite, according to an embodiment. In a firstscenario, such as, upon initially powering up the hub and provisioningthe hub, a first step 1010 includes the hub connecting, for example, tothe satellite. Upon connecting, a second step 1020 includes downloadinga hub or data profile to the hub. In a second more frequently occurringscenario, a third step 1030 includes the hub connecting, for example, tothe satellite. A fourth step 1040 includes sharing the hub profile to,for example, data devices associated with the hub. A fifth step 1050includes requesting synchronization parameters which can be used forvirtual preamble allocation and usage as shown in step 830.

FIG. 11 is a flow chart that includes steps of a method of datareporting, according to an embodiment. A first step 1110 includesreceiving, by a base station, one or more preambles from a set of one ormore data sources during a scheduled time slot. A second step 1120includes receiving, by the base station, one or more virtual preamblesfrom a network server during the scheduled time slot, wherein the one ormore virtual preambles are associated with another set of one or moredata sources. A third step 1130 includes generating, by the basestation, responses to the preambles and the virtual preambles, whereinthe responses included scheduled time and frequency allocations foruplink communication from the set of one or more data sources and theother set of one or more data sources. A fourth step 1140 includestransmitting, by the base station, the responses to the set of one ormore data sources and the other set of one or more data sources.

After the base station responds to the preambles and the virtualpreambles, the base station receives uplink wireless communication fromthe set of one or more data sources and the other set of one or moredata sources according to the scheduled time and frequency allocations.

For at least some embodiments, the slots (for example, time slots of aschedule) for the preambles and virtual preambles are pre-allocated by anetwork of the base station to different data sources based on datatransmission requirements of the different data sources. For anembodiment, the hubs of the data sources share timing synchronizationperformance with the base station and base station shares the timingsynchronization performance with the network server, and wherein thenetwork server further allocates the slots for the preambles and virtualpreambles based on the timing synchronization performance.

As previously described, for an embodiment, the timing synchronizationperformance includes a wireless uplink timing synchronization accuracybetween the hub and the base station. If timing synchronizationperformance is better than a threshold then virtual preamble can beallocated, and if it is worse than the threshold, than preambles areallocated. In order to support virtual preambles, uplink timingsynchronization should be such that a “timing advance command” messageis not required from the base station. For an embodiment, the basestation estimates an uplink timing error and sends it to hub as a timingadvance. The hub uses timing advance to correct its uplink transmissiontiming for further uplink transmissions. In case of virtual preambles,since there is no over the air virtual preamble, the timing error is notestimated by base station. Thus, in order to support virtual preambles,uplink timing synchronization accuracy should be better than the definedthreshold. The timing accuracy performance depends upon the capabilityof the hub to track satellite position and/or measure accurate roundtrip time of wireless communication between hub and base station throughthe satellite. That is, for an embodiment, the timing synchronizationperformance can be determined by measuring the round trip time betweenthe hub and the base station.

For an embodiment, the network server defines a trigger function of thevirtual preambles. For an embodiment, slots for the preambles and thevirtual preambles are pre-allocated based on a level of deterministiccoordination of the trigger function of the virtual preambles. For anembodiment, the one or more data source communicate with the basestation through one or more hubs, and wherein at least one of the hubsinclude a plurality of triggers, and preambles and virtual preambles areassigned to the at least one hub based on the plurality of triggers.

For an embodiment, the preambles and the virtual preambles providenotice to the base station that a hub associated with at least one datasource needs to transmit over the uplink wireless link.

For an embodiment, the network server temporally coordinates the virtualpreambles with the scheduled time slots, and the network servertemporally coordinates the preambles with the scheduled time slots. Foran embodiment, the network server additionally temporally coordinatingthe virtual preambles with a propagation air-time between the basestation and a hub associated with at least one data source. For anembodiment, this includes the network server conveying to the hubscheduled time slots for both preambles and virtual preambles along withtrigger functions.

For an embodiment, the responses include a preamble ID or a virtualpreamble ID. For an embodiment, the responses further include a timeduration in which a scrambling code is valid. For an embodiment, thescrambling code includes an RNTI (Radio Network Temporary Identifier).For an embodiment, the preambles and the virtual preambles each includeidentifying information, and further comprising identifying, by the basestation, a resource allocation size (number of time and frequency slots)based on the identifying information

FIG. 12 shows data profiles, according to an embodiment. The dataprofiles provide coordination of the communication of the data of thedata devices over the shared wireless satellite links. The communicationcan include one or more of real time data reporting, scheduled datareporting, and/or periodic data reporting. The data profile for a givendata device provides the hub associated with the data device the abilityto control a timing of communication of the data for each of the one ormore data sources from the hub to a base station through the wirelesssatellite link. The controlled timing provides for synchronization ofthe communication of the data with respect to the communication of dataof other data source of both the same hub, and for one or more differenthubs. For an embodiment, the data profile additionally provides the hubwith a frequency allocation for the communication of the data of thedata source.

An exemplary generic data profile 1210 of FIG. 12 includes enablement ofreal time access or real time reporting of the data of the data device,enablement of scheduled access or scheduled reporting of the data of thedata device, and enablement of periodic access or periodic reporting ofthe data of the data device. Further, for an embodiment, the dataprofile also includes an estimated MCS (modulation and coding scheme).Further, for an embodiment, the data profile also includes a dataprocessing function.

A specific example of a data profile 1220 provides for reporting of thelocation of a data device. This could be, for example, the reporting ofdata of a data device associated with a vehicle. For this embodiment,both the real time data reporting and the periodic data reporting areenabled, but the scheduled reporting is not enabled. Specifically, theperiodic reporting is specified to report once every 15 minutes,beginning and 12:00 (noon). Further, the reporting packet includes amessage size of 16 bytes, wherein the preamble codes and the MCS arespecified. The data profile 1220 includes a specific data processingfunction. The exemplary function includes determining whether the datadevice (and therefore, the vehicle associated with the data device) iswithin a geographical fence. While the data device is within thegeographical fence, the data device follows the periodic reportingschedule as specified by the data profile. If the data device isdetected to leave an area specified by the geographical fence, the realtime reporting flag is triggered, and the hub of the data deviceperformed real time communication with the base station that includes,for example, the location of the data device as detected outside of thegeographical fence.

FIG. 13 shows a plurality of hubs 1310, 1390 that communicate data ofdata sources 1311, 1312, 1313, 1314, 1315 through a shared resource to abase station, according to an embodiment. As shown, the data sources1311, 1312, 1313, 1314, 1315 are connected to the hubs 1310, 1390. Thehubs 1310, 1390 communicate through modems 1330, 1332 to a modem 1345 ofthe base station 1340 through the wireless links. For an embodiment, thewireless links are a shared resource 1399 that has a limited capacity.The described embodiments include data profiles which are utilized toprovide efficient use of the shared resource 1399. The base station mayalso communicate with outside networks 1370, 1380.

As previously described, it is to be understood that the data sources1311, 1312, 1313, 1314, 1315 can vary in type, and can each require verydifferent data reporting characteristics. The shared resource 1399 is alimited resource, and the use of this limited resource should bejudicious and efficient. In order to efficiently utilize the sharedresource 1399, each of the data sources 1311, 1312, 1313, 1314, 1315 areprovided with data profiles 1321, 1322, 1323, 1324, 1325 that coordinatethe timing (and/or frequency) of reporting (communication by the hubs1310, 1390 to the base station 1340 through the shared resource 1399) ofthe data provided by the data sources 1311, 1312, 1313, 1314, 1315.

For an embodiment, a network management element 1350 maintains adatabase 1360 in which the data profiles 1321, 1322, 1323, 1324, 1325can be stored and maintained. Further, the network management element1350 manages the data profiles 1321, 1322, 1323, 1324, 1325, wherein themanagement includes ensuring that synchronization is maintained duringthe data reporting by the hubs 1310, 1390 of the data of each of thedata sources 1311, 1312, 1313, 1314, 1315. That is, the data reported byeach hub 1310, 1390 of the data of the data sources 1311, 1312, 1313,1314, 1315 maintains synchronization of the data reporting of each ofthe data sources 1311, 1312, 1313, 1314, 1315 relative to each other.Again, the network management element 1350 ensures this synchronizationthrough management of the data profiles 1321, 1322, 1323, 1324, 1325.The synchronization between the data sources 1311, 1312, 1313, 1314,1315 distributes the timing of the reporting of the data of each of thedata sources 1311, 1312, 1313, 1314, 1315 to prevent the reporting ofone device from interfering with the reporting of another device, andprovides for efficiency in the data reporting.

For at least some embodiments, the network management element 1350resides in a central network location perhaps collocated with multiplebase stations and/or co-located with a network operations center (asshown, for example, in FIG. 6). For an embodiment, the networkmanagement element 1350 directly communicates with the base station 1340and initiates the transfer of data profiles across the network via thebase station 1340 to the hubs 1310, 1390.

For at least some embodiments, data profiles are distributed when newhubs are brought onto the network, when hubs change ownership, or whenthe hubs are re-provisioned. Other changes to data profile contentsoutside of these situations are more likely addressed by sync packets(for an embodiment, a sync packet is a packet to update the value of aspecific field inside of a data profile, but not necessarily updatingthe structure of the data profile) were only small changes to profilefields are required.

As described, the data profiles 1321, 1322, 1323, 1324, 1325 controltiming of when the hubs 1310, 1390 communicate the data of the datasources 1311, 1312, 1313, 1314, 1315 through the shared resource 1399.Accordingly, the described embodiments coordinate access to the sharedresource 1399 to insure optimal usage of the network resource to avoidcollisions between packets, the transmission of redundant information,and to reshape undesired traffic profiles.

Although specific embodiments have been described and illustrated, theembodiments are not to be limited to the specific forms or arrangementsof parts so described and illustrated. The described embodiments are toonly be limited by the claims.

What is claimed:
 1. A method of dynamically estimating a propagationtime between a first node and a second node of a wireless network,comprising: a. receiving, by the second node, from the first node apacket containing a first timestamp representing the transmit time ofthe packet; b. receiving, by the second node, from a local time source,a second timestamp corresponding with a time of reception of the firsttimestamp received from the first node; c. calculating a time differencebetween the first timestamp and the second timestamp; d. storing thetime difference between the first timestamp and the second timestamp; e.calculating, by the second node, a predictive model for predicting thepropagation time based the time difference between the first timestampand the second timestamp; and f. estimating, by the second node, thepropagation time between the first node and the second node at a time,comprising querying the predictive model with the time.
 2. The method ofclaim 1, further comprising: storing a time of the calculating of thetime difference; wherein calculating, by the second node, the predictivemodel is further based on the time of calculating the time difference.3. The method of claim 1, further comprising: performing steps a, b, c,d, N successive times for N packets; and calculating, by the secondnode, the predictive model for predicting the propagation time, based ontime differences between the first timestamps and the second timestampsof each of the N packets.
 4. The method of claim 3, wherein the Npackets are a running number of packets of a continuous series ofpackets.
 5. The method of claim 1, further comprising: estimating, bythe second node, additional first time stamps of additional packetsbased on the first time stamp, and forward integrating counterinformation provided by the first node
 6. The method of claim 1, whereincalculating, by the second node, the predictive model for predicting thepropagation time based the time difference between the first timestampand the second timestamp comprises: selecting a regression model base ona-priori information about characteristics including a cyclic nature ofthe propagation delay between the first node and the second node; andcomputing parameters of the regression model based on at least the timedifference between the first timestamp and the second timestamp.
 7. Themethod of claim 1, further comprising identifying a time recentthreshold between a time of a last time difference calculation and thetime, wherein the time recent threshold is identified based on anestimated rate of change of system dynamics of the first node and thesecond node.
 8. The method of claim 1, wherein the first node comprisesa base station and the second node comprises a hub.
 9. The method ofclaim 8, wherein a wireless communication link is formed between thebase station and the hub.
 10. The method of claim 1, further comprising,the second node, using the predicted propagation time to facilitatewireless communication between the second node and the first nodecomprising synchronizing reception timing of wireless communication fromthe second node to the first node.
 11. The method of claim 1, whereinhow frequently additional first timestamp and second time stamps arereceived by the second node is selected based on a determined error inestimates of the propagation delay.
 12. The method of claim 1, whereinhow frequently additional first timestamp and second time stamps arereceived by the second node is selected based on at least one of user orhub-initiated commands and configurations, or an ephemeris of asatellite within a wireless link between the second node and the firstnode.
 13. The method of claim 1, further comprising selecting howfrequently to estimate the propagation delay between the first node andthe second node at additional times, and accordingly querying thepredictive model with the future times based on a determined error inestimates of the propagation delay.
 14. The method of claim 1, furthercomprising selecting how frequently to estimate the propagation delaybetween the first node and the second node at additional times, andaccordingly querying the predictive model with the future times based onat least one of user or hub-initiated commands and configurations, or anephemeris of a satellite within a wireless link between the second nodeand the first node.
 15. The method of claim 1, further comprisingupdating the predictive model based on a determined error in estimatesof the propagation delay.
 16. The method of claim 1, further comprisingupdating the predictive model based on at least one of user orhub-initiated commands and configurations, or an ephemeris of asatellite within a wireless link between the second node and the firstnode.
 17. The method of claim 1, wherein when the estimated propagationdelay is within an error estimate threshold, then further comprisingreceiving, first node, one or more preambles from a set of one or moredata sources of the second node during a scheduled time slot; receiving,by the first node, one or more virtual preambles from a network serverduring the scheduled time slot, wherein the one or more virtualpreambles are associated with another set of one or more data sources ofthe second node; generating, by the first node, responses to thepreambles and the virtual preambles, wherein the responses includedscheduled time and frequency allocations for uplink communication fromthe set of one or more data sources and the other set of one or moredata sources; and transmitting, by the first node, the responses to theset of one or more data sources and the other set of one or more datasources.
 18. A node operative to: a. receive from a first node a packetcontaining a first timestamp representing the transmit time of thepacket; b. receive from a local time source, a second timestampcorresponding with a time of reception of the first timestamp receivedfrom the first node; c. calculate a time difference between the firsttimestamp and the second timestamp; d. store the time difference betweenthe first timestamp and the second timestamp; e. calculate a predictivemodel for predicting the propagation time based the time differencebetween the first timestamp and the second timestamp; and f. estimatethe propagation time between the first node and the second node at atime, comprising querying the predictive model with the time.
 19. Thenode of claim 18, wherein the node is further operative to: performsteps a, b, c, d, N successive times for N packets; and calculate thepredictive model for predicting the propagation time, based on timedifferences between the first timestamps and the second timestamps ofeach of the N packets.
 20. The node of claim 18, wherein the node isfurther operative to selecting how frequently to estimate thepropagation delay between the first node and the second node atadditional times, and accordingly querying the predictive model with thefuture times based on a determined error in estimates of the propagationdelay.